2,576 research outputs found
Mathematics and the Internet: A Source of Enormous Confusion and Great Potential
Graph theory models the Internet mathematically, and a number of plausible mathematically intersecting network models for the Internet have been developed and studied. Simultaneously, Internet researchers have developed methodology to use real data to validate, or invalidate, proposed Internet models. The authors look at these parallel developments, particularly as they apply to scale-free network models of the preferential attachment type
Robustness of airline alliance route networks
The aim of this study is to analyze the robustness of the three major airline alliances’ (i.e., Star Alliance, oneworld and SkyTeam) route networks. Firstly, the normalization of a multi-scale measure of vulnerability is proposed in order to perform the analysis in networks with different sizes, i.e., number of nodes. An alternative node selection criterion is also proposed in order to study robustness and vulnerability of such complex networks, based on network efficiency. And lastly, a new procedure – the inverted adaptive strategy – is presented to sort the nodes in order to anticipate network breakdown. Finally, the robustness of the three alliance networks are analyzed with (1) a normalized multi-scale measure of vulnerability, (2) an adaptive strategy based on four different criteria and (3) an inverted adaptive strategy based on the efficiency criterion. The results show that Star Alliance has the most resilient route network, followed by SkyTeam and then oneworld. It was also shown that the inverted adaptive strategy based on the efficiency criterion – inverted efficiency – shows a great success in quickly breaking networks similar to that found with betweenness criterion but with even better results.Peer ReviewedPostprint (author’s final draft
Software systems through complex networks science: Review, analysis and applications
Complex software systems are among most sophisticated human-made systems, yet
only little is known about the actual structure of 'good' software. We here
study different software systems developed in Java from the perspective of
network science. The study reveals that network theory can provide a prominent
set of techniques for the exploratory analysis of large complex software
system. We further identify several applications in software engineering, and
propose different network-based quality indicators that address software
design, efficiency, reusability, vulnerability, controllability and other. We
also highlight various interesting findings, e.g., software systems are highly
vulnerable to processes like bug propagation, however, they are not easily
controllable
Dynamics of Tipping Cascades on Complex Networks
Tipping points occur in diverse systems in various disciplines such as
ecology, climate science, economy or engineering. Tipping points are critical
thresholds in system parameters or state variables at which a tiny perturbation
can lead to a qualitative change of the system. Many systems with tipping
points can be modeled as networks of coupled multistable subsystems, e.g.
coupled patches of vegetation, connected lakes, interacting climate tipping
elements or multiscale infrastructure systems. In such networks, tipping events
in one subsystem are able to induce tipping cascades via domino effects. Here,
we investigate the effects of network topology on the occurrence of such
cascades. Numerical cascade simulations with a conceptual dynamical model for
tipping points are conducted on Erd\H{o}s-R\'enyi, Watts-Strogatz and
Barab\'asi-Albert networks. Additionally, we generate more realistic networks
using data from moisture-recycling simulations of the Amazon rainforest and
compare the results to those obtained for the model networks. We furthermore
use a directed configuration model and a stochastic block model which preserve
certain topological properties of the Amazon network to understand which of
these properties are responsible for its increased vulnerability. We find that
clustering and spatial organization increase the vulnerability of networks and
can lead to tipping of the whole network. These results could be useful to
evaluate which systems are vulnerable or robust due to their network topology
and might help to design or manage systems accordingly.Comment: 22 pages, 12 figure
Centrality scaling in large networks
Betweenness centrality lies at the core of both transport and structural
vulnerability properties of complex networks, however, it is computationally
costly, and its measurement for networks with millions of nodes is near
impossible. By introducing a multiscale decomposition of shortest paths, we
show that the contributions to betweenness coming from geodesics not longer
than L obey a characteristic scaling vs L, which can be used to predict the
distribution of the full centralities. The method is also illustrated on a
real-world social network of 5.5*10^6 nodes and 2.7*10^7 links
Multiscale vulnerability of complex networks
We present a novel approach to quantify the vulnerability of a complex network, i.e., the capacity of a graph to maintain its functional performance under random damages or malicious attacks. The proposed measure represents a multiscale evaluation of vulnerability, and makes use of combined powers of the links' betweenness. We show that the proposed approach is able to properly describe some cases for which earlier measures of vulnerability fail. The relevant applications of our method for technological network design are outlined
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